Overview

Dataset statistics

Number of variables21
Number of observations38802
Missing cells0
Missing cells (%)0.0%
Duplicate rows4901
Duplicate rows (%)12.6%
Total size in memory6.2 MiB
Average record size in memory168.0 B

Variable types

Categorical9
Numeric8
Text3
DateTime1

Alerts

OrderCount has constant value ""Constant
Country_y has constant value ""Constant
Dataset has 4901 (12.6%) duplicate rowsDuplicates
Employee is highly overall correlated with State and 3 other fieldsHigh correlation
ListPrice is highly overall correlated with Sales and 4 other fieldsHigh correlation
Sales is highly overall correlated with ListPrice and 3 other fieldsHigh correlation
SaleswithStandard is highly overall correlated with ListPrice and 3 other fieldsHigh correlation
StandardCost is highly overall correlated with ListPrice and 4 other fieldsHigh correlation
State is highly overall correlated with Employee and 3 other fieldsHigh correlation
StateCD is highly overall correlated with Employee and 3 other fieldsHigh correlation
State_duplicated_0 is highly overall correlated with Employee and 3 other fieldsHigh correlation
UnitPrice is highly overall correlated with ListPrice and 4 other fieldsHigh correlation
productcategory is highly overall correlated with ListPrice and 3 other fieldsHigh correlation
productsubcategory is highly overall correlated with productcategoryHigh correlation
saleterritory is highly overall correlated with Employee and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-01-10 00:21:51.021898
Analysis finished2024-01-10 00:21:57.455040
Duration6.43 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

productcategory
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
Bikes
16453 
Components
11904 
Clothing
7449 
Accessories
2996 

Length

Max length11
Median length10
Mean length7.5731406
Min length5

Characters and Unicode

Total characters293853
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClothing
2nd rowClothing
3rd rowComponents
4th rowClothing
5th rowClothing

Common Values

ValueCountFrequency (%)
Bikes 16453
42.4%
Components 11904
30.7%
Clothing 7449
19.2%
Accessories 2996
 
7.7%

Length

2024-01-09T19:21:57.499579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T19:21:57.565873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
bikes 16453
42.4%
components 11904
30.7%
clothing 7449
19.2%
accessories 2996
 
7.7%

Most occurring characters

ValueCountFrequency (%)
s 37345
12.7%
e 34349
11.7%
o 34253
11.7%
n 31257
10.6%
i 26898
9.2%
C 19353
6.6%
t 19353
6.6%
B 16453
 
5.6%
k 16453
 
5.6%
p 11904
 
4.1%
Other values (7) 46235
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 255051
86.8%
Uppercase Letter 38802
 
13.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 37345
14.6%
e 34349
13.5%
o 34253
13.4%
n 31257
12.3%
i 26898
10.5%
t 19353
7.6%
k 16453
6.5%
p 11904
 
4.7%
m 11904
 
4.7%
l 7449
 
2.9%
Other values (4) 23886
9.4%
Uppercase Letter
ValueCountFrequency (%)
C 19353
49.9%
B 16453
42.4%
A 2996
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 293853
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 37345
12.7%
e 34349
11.7%
o 34253
11.7%
n 31257
10.6%
i 26898
9.2%
C 19353
6.6%
t 19353
6.6%
B 16453
 
5.6%
k 16453
 
5.6%
p 11904
 
4.1%
Other values (7) 46235
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 37345
12.7%
e 34349
11.7%
o 34253
11.7%
n 31257
10.6%
i 26898
9.2%
C 19353
6.6%
t 19353
6.6%
B 16453
 
5.6%
k 16453
 
5.6%
p 11904
 
4.1%
Other values (7) 46235
15.7%

productsubcategory
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
Road Bikes
9224 
Mountain Bikes
5137 
Road Frames
3145 
Mountain Frames
3032 
Jerseys
2279 
Other values (28)
15985 

Length

Max length17
Median length14
Mean length10.086284
Min length4

Characters and Unicode

Total characters391368
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaps
2nd rowCaps
3rd rowMountain Frames
4th rowJerseys
5th rowJerseys

Common Values

ValueCountFrequency (%)
Road Bikes 9224
23.8%
Mountain Bikes 5137
13.2%
Road Frames 3145
 
8.1%
Mountain Frames 3032
 
7.8%
Jerseys 2279
 
5.9%
Touring Bikes 2092
 
5.4%
Helmets 1723
 
4.4%
Wheels 1327
 
3.4%
Gloves 1271
 
3.3%
Handlebars 984
 
2.5%
Other values (23) 8588
22.1%

Length

2024-01-09T19:21:57.621967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bikes 16453
26.0%
road 12369
19.5%
mountain 8169
12.9%
frames 6874
10.9%
touring 2789
 
4.4%
jerseys 2279
 
3.6%
helmets 1723
 
2.7%
wheels 1327
 
2.1%
gloves 1271
 
2.0%
handlebars 984
 
1.6%
Other values (28) 9106
14.4%

Most occurring characters

ValueCountFrequency (%)
s 42251
 
10.8%
e 40814
 
10.4%
a 34047
 
8.7%
i 29358
 
7.5%
o 27762
 
7.1%
24542
 
6.3%
n 21165
 
5.4%
k 18643
 
4.8%
B 18003
 
4.6%
r 16008
 
4.1%
Other values (27) 118775
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 302794
77.4%
Uppercase Letter 63535
 
16.2%
Space Separator 24542
 
6.3%
Dash Punctuation 497
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42251
14.0%
e 40814
13.5%
a 34047
11.2%
i 29358
9.7%
o 27762
9.2%
n 21165
7.0%
k 18643
6.2%
r 16008
 
5.3%
d 15931
 
5.3%
t 14053
 
4.6%
Other values (10) 42762
14.1%
Uppercase Letter
ValueCountFrequency (%)
B 18003
28.3%
R 12602
19.8%
M 8169
12.9%
F 7016
 
11.0%
T 3581
 
5.6%
H 3067
 
4.8%
S 2534
 
4.0%
J 2279
 
3.6%
C 1563
 
2.5%
W 1327
 
2.1%
Other values (5) 3394
 
5.3%
Space Separator
ValueCountFrequency (%)
24542
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 497
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 366329
93.6%
Common 25039
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42251
11.5%
e 40814
 
11.1%
a 34047
 
9.3%
i 29358
 
8.0%
o 27762
 
7.6%
n 21165
 
5.8%
k 18643
 
5.1%
B 18003
 
4.9%
r 16008
 
4.4%
d 15931
 
4.3%
Other values (25) 102347
27.9%
Common
ValueCountFrequency (%)
24542
98.0%
- 497
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 391368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 42251
 
10.8%
e 40814
 
10.4%
a 34047
 
8.7%
i 29358
 
7.5%
o 27762
 
7.1%
24542
 
6.3%
n 21165
 
5.4%
k 18643
 
4.8%
B 18003
 
4.6%
r 16008
 
4.1%
Other values (27) 118775
30.3%

saleterritory
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
Southwest
13379 
Northwest
7865 
Southeast
5937 
Central
5812 
Northeast
5809 

Length

Max length9
Median length9
Mean length8.7004278
Min length7

Characters and Unicode

Total characters337594
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthwest
2nd rowNorthwest
3rd rowNorthwest
4th rowNorthwest
5th rowNorthwest

Common Values

ValueCountFrequency (%)
Southwest 13379
34.5%
Northwest 7865
20.3%
Southeast 5937
15.3%
Central 5812
15.0%
Northeast 5809
15.0%

Length

2024-01-09T19:21:57.677565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T19:21:57.739636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
southwest 13379
34.5%
northwest 7865
20.3%
southeast 5937
15.3%
central 5812
15.0%
northeast 5809
15.0%

Most occurring characters

ValueCountFrequency (%)
t 71792
21.3%
e 38802
11.5%
o 32990
9.8%
h 32990
9.8%
s 32990
9.8%
w 21244
 
6.3%
r 19486
 
5.8%
S 19316
 
5.7%
u 19316
 
5.7%
a 17558
 
5.2%
Other values (4) 31110
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 298792
88.5%
Uppercase Letter 38802
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 71792
24.0%
e 38802
13.0%
o 32990
11.0%
h 32990
11.0%
s 32990
11.0%
w 21244
 
7.1%
r 19486
 
6.5%
u 19316
 
6.5%
a 17558
 
5.9%
n 5812
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 19316
49.8%
N 13674
35.2%
C 5812
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 337594
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 71792
21.3%
e 38802
11.5%
o 32990
9.8%
h 32990
9.8%
s 32990
9.8%
w 21244
 
6.3%
r 19486
 
5.8%
S 19316
 
5.7%
u 19316
 
5.7%
a 17558
 
5.2%
Other values (4) 31110
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 71792
21.3%
e 38802
11.5%
o 32990
9.8%
h 32990
9.8%
s 32990
9.8%
w 21244
 
6.3%
r 19486
 
5.8%
S 19316
 
5.7%
u 19316
 
5.7%
a 17558
 
5.2%
Other values (4) 31110
9.2%

zip
Real number (ℝ)

Distinct302
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66052.637
Minimum2062
Maximum99337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:57.909343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2062
5-th percentile6460
Q139501
median77840
Q391801
95-th percentile98225
Maximum99337
Range97275
Interquartile range (IQR)52300

Descriptive statistics

Standard deviation30207.247
Coefficient of variation (CV)0.45732083
Kurtosis-0.84422923
Mean66052.637
Median Absolute Deviation (MAD)19382
Skewness-0.69798759
Sum2.5629744 × 109
Variance9.1247775 × 108
MonotonicityNot monotonic
2024-01-09T19:21:57.972444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98104 817
 
2.1%
75040 712
 
1.8%
77003 677
 
1.7%
85004 670
 
1.7%
75061 654
 
1.7%
33127 648
 
1.7%
63103 601
 
1.5%
3064 595
 
1.5%
38103 592
 
1.5%
78204 521
 
1.3%
Other values (292) 32315
83.3%
ValueCountFrequency (%)
2062 21
 
0.1%
2093 21
 
0.1%
2184 31
 
0.1%
2368 61
 
0.2%
2889 17
 
< 0.1%
2895 1
 
< 0.1%
3064 595
1.5%
3106 10
 
< 0.1%
3276 279
0.7%
3865 380
1.0%
ValueCountFrequency (%)
99337 3
 
< 0.1%
99202 165
0.4%
98926 286
0.7%
98801 11
 
< 0.1%
98671 11
 
< 0.1%
98632 228
0.6%
98626 166
0.4%
98584 10
 
< 0.1%
98532 65
 
0.2%
98503 358
0.9%

State
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
California
6920 
Texas
4785 
Washington
4574 
Florida
 
1851
Colorado
 
1673
Other values (30)
18999 

Length

Max length14
Median length12
Mean length8.2941086
Min length4

Characters and Unicode

Total characters321828
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWyoming
2nd rowWashington
3rd rowWashington
4th rowWyoming
5th rowWashington

Common Values

ValueCountFrequency (%)
California 6920
17.8%
Texas 4785
 
12.3%
Washington 4574
 
11.8%
Florida 1851
 
4.8%
Colorado 1673
 
4.3%
Michigan 1389
 
3.6%
New York 1370
 
3.5%
Missouri 1284
 
3.3%
New Hampshire 1264
 
3.3%
Tennessee 1212
 
3.1%
Other values (25) 12480
32.2%

Length

2024-01-09T19:21:58.031681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 6920
16.1%
texas 4785
 
11.1%
washington 4574
 
10.6%
new 3157
 
7.3%
florida 1851
 
4.3%
colorado 1673
 
3.9%
michigan 1389
 
3.2%
york 1370
 
3.2%
missouri 1284
 
3.0%
hampshire 1264
 
2.9%
Other values (28) 14785
34.3%

Most occurring characters

ValueCountFrequency (%)
a 39582
12.3%
i 38720
12.0%
o 30523
 
9.5%
n 30512
 
9.5%
r 19724
 
6.1%
e 19388
 
6.0%
s 19264
 
6.0%
l 12399
 
3.9%
C 10462
 
3.3%
h 10358
 
3.2%
Other values (33) 90896
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 274526
85.3%
Uppercase Letter 43052
 
13.4%
Space Separator 4250
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39582
14.4%
i 38720
14.1%
o 30523
11.1%
n 30512
11.1%
r 19724
7.2%
e 19388
7.1%
s 19264
7.0%
l 12399
 
4.5%
h 10358
 
3.8%
t 9531
 
3.5%
Other values (14) 44525
16.2%
Uppercase Letter
ValueCountFrequency (%)
C 10462
24.3%
T 5997
13.9%
W 5331
12.4%
N 4619
10.7%
M 4603
10.7%
O 1947
 
4.5%
F 1851
 
4.3%
I 1563
 
3.6%
Y 1370
 
3.2%
H 1264
 
2.9%
Other values (8) 4045
 
9.4%
Space Separator
ValueCountFrequency (%)
4250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 317578
98.7%
Common 4250
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39582
12.5%
i 38720
12.2%
o 30523
 
9.6%
n 30512
 
9.6%
r 19724
 
6.2%
e 19388
 
6.1%
s 19264
 
6.1%
l 12399
 
3.9%
C 10462
 
3.3%
h 10358
 
3.3%
Other values (32) 86646
27.3%
Common
ValueCountFrequency (%)
4250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 39582
12.3%
i 38720
12.0%
o 30523
 
9.5%
n 30512
 
9.5%
r 19724
 
6.1%
e 19388
 
6.0%
s 19264
 
6.0%
l 12399
 
3.9%
C 10462
 
3.3%
h 10358
 
3.2%
Other values (33) 90896
28.2%
Distinct390
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:58.285388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length29
Median length19
Mean length13.037575
Min length7

Characters and Unicode

Total characters505884
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowFrançois Ferrier
2nd rowRichard Bready
3rd rowCarolyn Farino
4th rowFrançois Ferrier
5th rowRichard Bready
ValueCountFrequency (%)
john 1354
 
1.7%
robert 1064
 
1.4%
liu 960
 
1.2%
richard 847
 
1.1%
michael 838
 
1.1%
mike 746
 
1.0%
kevin 573
 
0.7%
andrew 569
 
0.7%
allen 555
 
0.7%
mary 555
 
0.7%
Other values (621) 69836
89.7%
2024-01-09T19:21:58.577627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 49123
 
9.7%
e 46588
 
9.2%
n 40889
 
8.1%
39095
 
7.7%
r 37021
 
7.3%
i 32248
 
6.4%
o 25244
 
5.0%
l 21539
 
4.3%
t 18015
 
3.6%
s 16865
 
3.3%
Other values (45) 179257
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 385876
76.3%
Uppercase Letter 80051
 
15.8%
Space Separator 39095
 
7.7%
Dash Punctuation 696
 
0.1%
Other Punctuation 166
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 49123
12.7%
e 46588
12.1%
n 40889
10.6%
r 37021
9.6%
i 32248
 
8.4%
o 25244
 
6.5%
l 21539
 
5.6%
t 18015
 
4.7%
s 16865
 
4.4%
h 14798
 
3.8%
Other values (17) 83546
21.7%
Uppercase Letter
ValueCountFrequency (%)
C 7996
10.0%
M 7193
 
9.0%
J 6995
 
8.7%
D 6166
 
7.7%
A 6161
 
7.7%
B 6110
 
7.6%
K 5772
 
7.2%
L 4734
 
5.9%
S 4639
 
5.8%
H 4476
 
5.6%
Other values (14) 19809
24.7%
Other Punctuation
ValueCountFrequency (%)
¡ 110
66.3%
. 56
33.7%
Space Separator
ValueCountFrequency (%)
39095
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 696
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 465927
92.1%
Common 39957
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 49123
 
10.5%
e 46588
 
10.0%
n 40889
 
8.8%
r 37021
 
7.9%
i 32248
 
6.9%
o 25244
 
5.4%
l 21539
 
4.6%
t 18015
 
3.9%
s 16865
 
3.6%
h 14798
 
3.2%
Other values (41) 163597
35.1%
Common
ValueCountFrequency (%)
39095
97.8%
- 696
 
1.7%
¡ 110
 
0.3%
. 56
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 505484
99.9%
None 400
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 49123
 
9.7%
e 46588
 
9.2%
n 40889
 
8.1%
39095
 
7.7%
r 37021
 
7.3%
i 32248
 
6.4%
o 25244
 
5.0%
l 21539
 
4.3%
t 18015
 
3.6%
s 16865
 
3.3%
Other values (43) 178857
35.4%
None
ValueCountFrequency (%)
ç 290
72.5%
¡ 110
 
27.5%

Employee
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
Jillian Carson
7825 
Linda Mitchell
7107 
Michael Blythe
7069 
Tsvi Reiter
5417 
Shu Ito
4545 
Other values (4)
6839 

Length

Max length19
Median length14
Mean length13.107881
Min length7

Characters and Unicode

Total characters508612
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDavid Campbell
2nd rowDavid Campbell
3rd rowDavid Campbell
4th rowDavid Campbell
5th rowDavid Campbell

Common Values

ValueCountFrequency (%)
Jillian Carson 7825
20.2%
Linda Mitchell 7107
18.3%
Michael Blythe 7069
18.2%
Tsvi Reiter 5417
14.0%
Shu Ito 4545
11.7%
David Campbell 2247
 
5.8%
Pamela Ansman-Wolfe 2064
 
5.3%
Tete Mensa-Annan 1886
 
4.9%
Stephen Jiang 642
 
1.7%

Length

2024-01-09T19:21:58.655566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T19:21:58.729203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
jillian 7825
10.1%
carson 7825
10.1%
linda 7107
9.2%
mitchell 7107
9.2%
michael 7069
9.1%
blythe 7069
9.1%
tsvi 5417
 
7.0%
reiter 5417
 
7.0%
ito 4545
 
5.9%
shu 4545
 
5.9%
Other values (8) 13678
17.6%

Most occurring characters

ValueCountFrequency (%)
l 52624
 
10.3%
i 50656
 
10.0%
e 45396
 
8.9%
a 44926
 
8.8%
38802
 
7.6%
n 35713
 
7.0%
t 26666
 
5.2%
h 26432
 
5.2%
s 17192
 
3.4%
M 16062
 
3.2%
Other values (25) 154143
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 384306
75.6%
Uppercase Letter 81554
 
16.0%
Space Separator 38802
 
7.6%
Dash Punctuation 3950
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 52624
13.7%
i 50656
13.2%
e 45396
11.8%
a 44926
11.7%
n 35713
9.3%
t 26666
6.9%
h 26432
6.9%
s 17192
 
4.5%
o 14434
 
3.8%
c 14176
 
3.7%
Other values (10) 56091
14.6%
Uppercase Letter
ValueCountFrequency (%)
M 16062
19.7%
C 10072
12.4%
J 8467
10.4%
T 7303
9.0%
L 7107
8.7%
B 7069
8.7%
R 5417
 
6.6%
S 5187
 
6.4%
I 4545
 
5.6%
A 3950
 
4.8%
Other values (3) 6375
 
7.8%
Space Separator
ValueCountFrequency (%)
38802
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3950
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 465860
91.6%
Common 42752
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 52624
 
11.3%
i 50656
 
10.9%
e 45396
 
9.7%
a 44926
 
9.6%
n 35713
 
7.7%
t 26666
 
5.7%
h 26432
 
5.7%
s 17192
 
3.7%
M 16062
 
3.4%
o 14434
 
3.1%
Other values (23) 135759
29.1%
Common
ValueCountFrequency (%)
38802
90.8%
- 3950
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 508612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 52624
 
10.3%
i 50656
 
10.0%
e 45396
 
8.9%
a 44926
 
8.8%
38802
 
7.6%
n 35713
 
7.0%
t 26666
 
5.2%
h 26432
 
5.2%
s 17192
 
3.4%
M 16062
 
3.2%
Other values (25) 154143
30.3%

OrderCount
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
1
38802 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38802
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 38802
100.0%

Length

2024-01-09T19:21:58.795914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T19:21:58.843184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 38802
100.0%

Most occurring characters

ValueCountFrequency (%)
1 38802
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38802
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38802
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38802
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38802
100.0%
Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
Minimum2011-05-31 00:00:00
Maximum2014-05-01 00:00:00
2024-01-09T19:21:58.888860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:58.948872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)

StandardCost
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474.68394
Minimum0.8565
Maximum2171.2942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:59.009666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.8565
5-th percentile8.2459
Q135.9596
median294.5797
Q3713.0798
95-th percentile1554.9479
Maximum2171.2942
Range2170.4377
Interquartile range (IQR)677.1202

Descriptive statistics

Standard deviation546.40695
Coefficient of variation (CV)1.1510963
Kurtosis0.74941848
Mean474.68394
Median Absolute Deviation (MAD)269.8338
Skewness1.2822201
Sum18418686
Variance298560.56
MonotonicityNot monotonic
2024-01-09T19:21:59.070484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
486.7066 3936
 
10.1%
13.0863 1723
 
4.4%
38.4923 1654
 
4.3%
713.0798 1305
 
3.4%
1554.9479 1134
 
2.9%
1251.9813 1048
 
2.7%
1265.6195 960
 
2.5%
187.1571 955
 
2.5%
461.4448 884
 
2.3%
360.9428 829
 
2.1%
Other values (80) 24374
62.8%
ValueCountFrequency (%)
0.8565 78
 
0.2%
1.8663 228
 
0.6%
2.9733 220
 
0.6%
3.3623 431
1.1%
3.3963 187
 
0.5%
6.9223 759
2.0%
8.2459 173
 
0.4%
8.9866 127
 
0.3%
9.1593 659
1.7%
10.3125 169
 
0.4%
ValueCountFrequency (%)
2171.2942 428
 
1.1%
1912.1544 593
1.5%
1898.0944 604
1.6%
1554.9479 1134
2.9%
1518.7864 427
 
1.1%
1481.9379 803
2.1%
1265.6195 960
2.5%
1251.9813 1048
2.7%
1082.51 665
1.7%
884.7083 687
1.8%

UnitPrice
Real number (ℝ)

HIGH CORRELATION 

Distinct220
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean464.63949
Minimum1.374
Maximum2146.962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:59.138006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.374
5-th percentile11.994
Q136.447
median323.994
Q3672.294
95-th percentile1466.01
Maximum2146.962
Range2145.588
Interquartile range (IQR)635.847

Descriptive statistics

Standard deviation535.43009
Coefficient of variation (CV)1.152356
Kurtosis1.2506734
Mean464.63949
Median Absolute Deviation (MAD)291.6
Skewness1.3998869
Sum18028941
Variance286685.38
MonotonicityNot monotonic
2024-01-09T19:21:59.197585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
469.794 2130
 
5.5%
419.4589 1696
 
4.4%
28.8404 1136
 
2.9%
20.1865 1057
 
2.7%
323.994 1056
 
2.7%
1466.01 968
 
2.5%
202.332 798
 
2.1%
183.9382 723
 
1.9%
874.794 686
 
1.8%
1430.442 684
 
1.8%
Other values (210) 27868
71.8%
ValueCountFrequency (%)
1.374 78
 
0.2%
2.495 1
 
< 0.1%
2.7445 7
 
< 0.1%
2.8942 15
 
< 0.1%
2.994 205
0.5%
3.975 1
 
< 0.1%
4.3221 1
 
< 0.1%
4.3725 8
 
< 0.1%
4.495 3
 
< 0.1%
4.611 20
 
0.1%
ValueCountFrequency (%)
2146.962 428
1.1%
2039.994 537
1.4%
2024.994 544
1.4%
1971.9942 3
 
< 0.1%
1957.4942 5
 
< 0.1%
1466.01 968
2.5%
1430.442 684
1.8%
1417.143 4
 
< 0.1%
1391.994 377
 
1.0%
1382.7606 9
 
< 0.1%

ListPrice
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.08191
Minimum2.29
Maximum3578.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:59.263879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile19.99
Q160.745
median539.99
Q31120.49
95-th percentile2443.35
Maximum3578.27
Range3575.98
Interquartile range (IQR)1059.745

Descriptive statistics

Standard deviation904.6905
Coefficient of variation (CV)1.1523517
Kurtosis1.1801335
Mean785.08191
Median Absolute Deviation (MAD)486
Skewness1.3904479
Sum30462748
Variance818464.9
MonotonicityNot monotonic
2024-01-09T19:21:59.325384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
782.99 2238
 
5.8%
699.0982 1698
 
4.4%
48.0673 1172
 
3.0%
33.6442 1164
 
3.0%
539.99 1061
 
2.7%
2443.35 972
 
2.5%
742.35 884
 
2.3%
2384.07 803
 
2.1%
337.22 802
 
2.1%
306.5636 724
 
1.9%
Other values (93) 27284
70.3%
ValueCountFrequency (%)
2.29 78
 
0.2%
4.99 228
 
0.6%
7.95 220
 
0.6%
8.6442 518
1.3%
8.99 672
1.7%
9.5 187
 
0.5%
19.99 173
 
0.4%
20.24 127
 
0.3%
23.5481 312
0.8%
24.49 347
0.9%
ValueCountFrequency (%)
3578.27 428
1.1%
3399.99 593
1.5%
3374.99 604
1.6%
2443.35 972
2.5%
2384.07 803
2.1%
2319.99 387
 
1.0%
2294.99 461
1.2%
2181.5625 589
1.5%
2071.4196 573
1.5%
2049.0982 587
1.5%

SaleswithStandard
Real number (ℝ)

HIGH CORRELATION 

Distinct1076
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1418.4338
Minimum0.8565
Maximum38530.385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:59.396382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.8565
5-th percentile20.3778
Q1107.8656
median486.7066
Q31737.2684
95-th percentile6075.1456
Maximum38530.385
Range38529.529
Interquartile range (IQR)1629.4028

Descriptive statistics

Standard deviation2273.1128
Coefficient of variation (CV)1.6025512
Kurtosis17.386995
Mean1418.4338
Median Absolute Deviation (MAD)447.4477
Skewness3.3513288
Sum55038068
Variance5167041.7
MonotonicityNot monotonic
2024-01-09T19:21:59.462594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
486.7066 1092
 
2.8%
973.4132 988
 
2.5%
1460.1198 696
 
1.8%
1946.8264 417
 
1.1%
187.1571 338
 
0.9%
461.4448 320
 
0.8%
26.1726 314
 
0.8%
713.0798 310
 
0.8%
76.9846 305
 
0.8%
1426.1596 298
 
0.8%
Other values (1066) 33724
86.9%
ValueCountFrequency (%)
0.8565 15
< 0.1%
1.713 11
 
< 0.1%
1.8663 20
0.1%
2.5695 6
 
< 0.1%
2.9733 26
0.1%
3.3623 34
0.1%
3.3963 13
 
< 0.1%
3.426 9
 
< 0.1%
3.7326 22
0.1%
4.2825 5
 
< 0.1%
ValueCountFrequency (%)
38530.3854 1
< 0.1%
32475.3 1
< 0.1%
31120.6959 1
< 0.1%
27062.75 1
< 0.1%
26770.1616 1
< 0.1%
26674.8822 1
< 0.1%
25192.9443 1
< 0.1%
24675.2272 1
< 0.1%
23711.0064 2
< 0.1%
22945.8528 2
< 0.1%

NetSales
Real number (ℝ)

Distinct1373
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-36.982001
Minimum-14846.244
Maximum1278.396
Zeros0
Zeros (%)0.0%
Negative19014
Negative (%)49.0%
Memory size303.3 KiB
2024-01-09T19:21:59.531865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-14846.244
5-th percentile-336.2385
Q1-57.9114
median0.6023
Q341.5086
95-th percentile250.0254
Maximum1278.396
Range16124.64
Interquartile range (IQR)99.42

Descriptive statistics

Standard deviation406.64194
Coefficient of variation (CV)-10.995672
Kurtosis298.05816
Mean-36.982001
Median Absolute Deviation (MAD)49.1085
Skewness-13.524238
Sum-1434975.6
Variance165357.67
MonotonicityNot monotonic
2024-01-09T19:21:59.596192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-67.2477 629
 
1.6%
-134.4954 506
 
1.3%
-33.8252 473
 
1.2%
-16.9126 455
 
1.2%
-50.7378 422
 
1.1%
-67.6504 274
 
0.7%
-201.7431 263
 
0.7%
-9.9143 261
 
0.7%
-16.0348 249
 
0.6%
-19.3038 244
 
0.6%
Other values (1363) 35026
90.3%
ValueCountFrequency (%)
-14846.24378 1
 
< 0.1%
-13496.58525 1
 
< 0.1%
-12236.90423 1
 
< 0.1%
-12146.92673 1
 
< 0.1%
-11504.568 1
 
< 0.1%
-10797.2682 3
< 0.1%
-10636.7664 1
 
< 0.1%
-9511.935 1
 
< 0.1%
-9447.609675 2
< 0.1%
-8157.93615 3
< 0.1%
ValueCountFrequency (%)
1278.396 4
 
< 0.1%
1268.996 4
 
< 0.1%
1263.745 5
 
< 0.1%
1250.127 6
< 0.1%
1150.5564 3
 
< 0.1%
1142.0964 10
< 0.1%
1137.3705 10
< 0.1%
1125.1143 9
< 0.1%
1022.7168 11
< 0.1%
1015.1968 14
< 0.1%

OrderQuantity
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4209577
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:59.665165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum44
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9568605
Coefficient of variation (CV)0.86433706
Kurtosis17.887214
Mean3.4209577
Median Absolute Deviation (MAD)1
Skewness3.1322146
Sum132740
Variance8.7430241
MonotonicityNot monotonic
2024-01-09T19:21:59.832746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 9469
24.4%
2 9234
23.8%
3 6532
16.8%
4 4773
12.3%
5 2769
 
7.1%
6 2033
 
5.2%
7 1047
 
2.7%
8 872
 
2.2%
9 513
 
1.3%
10 437
 
1.1%
Other values (31) 1123
 
2.9%
ValueCountFrequency (%)
1 9469
24.4%
2 9234
23.8%
3 6532
16.8%
4 4773
12.3%
5 2769
 
7.1%
6 2033
 
5.2%
7 1047
 
2.7%
8 872
 
2.2%
9 513
 
1.3%
10 437
 
1.1%
ValueCountFrequency (%)
44 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 1
 
< 0.1%
35 2
 
< 0.1%
34 3
< 0.1%
33 6
< 0.1%
32 5
< 0.1%

Sales
Real number (ℝ)

HIGH CORRELATION 

Distinct1297
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1381.4518
Minimum1.374
Maximum27893.619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size303.3 KiB
2024-01-09T19:21:59.895630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.374
5-th percentile25.9325
Q1131.976
median475.29
Q31622.2635
95-th percentile5864.04
Maximum27893.619
Range27892.245
Interquartile range (IQR)1490.2875

Descriptive statistics

Standard deviation2198.0183
Coefficient of variation (CV)1.591093
Kurtosis15.496543
Mean1381.4518
Median Absolute Deviation (MAD)418.7744
Skewness3.3061144
Sum53603093
Variance4831284.6
MonotonicityNot monotonic
2024-01-09T19:21:59.953151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
419.4589 629
 
1.6%
838.9178 506
 
1.3%
939.588 473
 
1.2%
469.794 455
 
1.2%
1409.382 422
 
1.1%
323.994 322
 
0.8%
183.9382 286
 
0.7%
647.988 283
 
0.7%
1879.176 274
 
0.7%
1258.3767 263
 
0.7%
Other values (1287) 34889
89.9%
ValueCountFrequency (%)
1.374 15
 
< 0.1%
2.748 11
 
< 0.1%
2.994 20
 
0.1%
4.122 6
 
< 0.1%
4.77 26
 
0.1%
5.1865 72
0.2%
5.394 57
0.1%
5.496 9
 
< 0.1%
5.7 13
 
< 0.1%
5.988 22
 
0.1%
ValueCountFrequency (%)
27893.619 1
< 0.1%
27055.76042 1
< 0.1%
26159.20808 1
< 0.1%
24938.47611 1
< 0.1%
23190.65179 2
< 0.1%
23020.13179 2
< 0.1%
22963.365 1
< 0.1%
22422.17835 1
< 0.1%
21176.50178 1
< 0.1%
21101.78748 2
< 0.1%

Country_y
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
US
38802 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77604
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
US 38802
100.0%

Length

2024-01-09T19:22:00.006575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-09T19:22:00.054021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
us 38802
100.0%

Most occurring characters

ValueCountFrequency (%)
U 38802
50.0%
S 38802
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77604
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 38802
50.0%
S 38802
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77604
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 38802
50.0%
S 38802
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 38802
50.0%
S 38802
50.0%
Distinct289
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
2024-01-09T19:22:00.218178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.2890573
Min length4

Characters and Unicode

Total characters321632
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowCasper
2nd rowSeattle
3rd rowPuyallup
4th rowCasper
5th rowSeattle
ValueCountFrequency (%)
city 1620
 
3.3%
seattle 817
 
1.7%
san 817
 
1.7%
saint 749
 
1.5%
new 721
 
1.5%
union 721
 
1.5%
garland 712
 
1.5%
miami 703
 
1.4%
lake 682
 
1.4%
houston 677
 
1.4%
Other values (308) 40516
83.1%
2024-01-09T19:22:00.462933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 29449
 
9.2%
a 27855
 
8.7%
o 25300
 
7.9%
n 24718
 
7.7%
l 23146
 
7.2%
i 20720
 
6.4%
t 18884
 
5.9%
r 17232
 
5.4%
s 14907
 
4.6%
9933
 
3.1%
Other values (41) 109488
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 262356
81.6%
Uppercase Letter 49039
 
15.2%
Space Separator 9933
 
3.1%
Dash Punctuation 304
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29449
11.2%
a 27855
10.6%
o 25300
9.6%
n 24718
9.4%
l 23146
8.8%
i 20720
7.9%
t 18884
 
7.2%
r 17232
 
6.6%
s 14907
 
5.7%
d 8839
 
3.4%
Other values (15) 51306
19.6%
Uppercase Letter
ValueCountFrequency (%)
C 6689
13.6%
S 6383
13.0%
M 4866
 
9.9%
L 4167
 
8.5%
N 2955
 
6.0%
G 2358
 
4.8%
H 2345
 
4.8%
B 2311
 
4.7%
P 2199
 
4.5%
R 1638
 
3.3%
Other values (14) 13128
26.8%
Space Separator
ValueCountFrequency (%)
9933
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 311395
96.8%
Common 10237
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29449
 
9.5%
a 27855
 
8.9%
o 25300
 
8.1%
n 24718
 
7.9%
l 23146
 
7.4%
i 20720
 
6.7%
t 18884
 
6.1%
r 17232
 
5.5%
s 14907
 
4.8%
d 8839
 
2.8%
Other values (39) 100345
32.2%
Common
ValueCountFrequency (%)
9933
97.0%
- 304
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29449
 
9.2%
a 27855
 
8.7%
o 25300
 
7.9%
n 24718
 
7.7%
l 23146
 
7.2%
i 20720
 
6.4%
t 18884
 
5.9%
r 17232
 
5.4%
s 14907
 
4.6%
9933
 
3.1%
Other values (41) 109488
34.0%

State_duplicated_0
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
California
6920 
Texas
4785 
Washington
4579 
Florida
 
1851
Colorado
 
1673
Other values (30)
18994 

Length

Max length14
Median length12
Mean length8.2943663
Min length4

Characters and Unicode

Total characters321838
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWyoming
2nd rowWashington
3rd rowWashington
4th rowWyoming
5th rowWashington

Common Values

ValueCountFrequency (%)
California 6920
17.8%
Texas 4785
 
12.3%
Washington 4579
 
11.8%
Florida 1851
 
4.8%
Colorado 1673
 
4.3%
Michigan 1384
 
3.6%
New York 1370
 
3.5%
Missouri 1284
 
3.3%
New Hampshire 1264
 
3.3%
Tennessee 1212
 
3.1%
Other values (25) 12480
32.2%

Length

2024-01-09T19:22:00.535922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 6920
16.1%
texas 4785
 
11.1%
washington 4579
 
10.6%
new 3157
 
7.3%
florida 1851
 
4.3%
colorado 1673
 
3.9%
michigan 1384
 
3.2%
york 1370
 
3.2%
missouri 1284
 
3.0%
hampshire 1264
 
2.9%
Other values (28) 14785
34.3%

Most occurring characters

ValueCountFrequency (%)
a 39582
12.3%
i 38715
12.0%
o 30528
 
9.5%
n 30517
 
9.5%
r 19724
 
6.1%
e 19388
 
6.0%
s 19269
 
6.0%
l 12399
 
3.9%
C 10462
 
3.3%
h 10358
 
3.2%
Other values (33) 90896
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 274536
85.3%
Uppercase Letter 43052
 
13.4%
Space Separator 4250
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39582
14.4%
i 38715
14.1%
o 30528
11.1%
n 30517
11.1%
r 19724
7.2%
e 19388
7.1%
s 19269
7.0%
l 12399
 
4.5%
h 10358
 
3.8%
t 9536
 
3.5%
Other values (14) 44520
16.2%
Uppercase Letter
ValueCountFrequency (%)
C 10462
24.3%
T 5997
13.9%
W 5336
12.4%
N 4619
10.7%
M 4598
10.7%
O 1947
 
4.5%
F 1851
 
4.3%
I 1563
 
3.6%
Y 1370
 
3.2%
H 1264
 
2.9%
Other values (8) 4045
 
9.4%
Space Separator
ValueCountFrequency (%)
4250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 317588
98.7%
Common 4250
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 39582
12.5%
i 38715
12.2%
o 30528
 
9.6%
n 30517
 
9.6%
r 19724
 
6.2%
e 19388
 
6.1%
s 19269
 
6.1%
l 12399
 
3.9%
C 10462
 
3.3%
h 10358
 
3.3%
Other values (32) 86646
27.3%
Common
ValueCountFrequency (%)
4250
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 39582
12.3%
i 38715
12.0%
o 30528
 
9.5%
n 30517
 
9.5%
r 19724
 
6.1%
e 19388
 
6.0%
s 19269
 
6.0%
l 12399
 
3.9%
C 10462
 
3.3%
h 10358
 
3.2%
Other values (33) 90896
28.2%

StateCD
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
CA
6920 
TX
4785 
WA
4579 
FL
 
1851
CO
 
1673
Other values (30)
18994 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77604
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWY
2nd rowWA
3rd rowWA
4th rowWY
5th rowWA

Common Values

ValueCountFrequency (%)
CA 6920
17.8%
TX 4785
 
12.3%
WA 4579
 
11.8%
FL 1851
 
4.8%
CO 1673
 
4.3%
MI 1384
 
3.6%
NY 1370
 
3.5%
MO 1284
 
3.3%
NH 1264
 
3.3%
TN 1212
 
3.1%
Other values (25) 12480
32.2%

Length

2024-01-09T19:22:00.585966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6920
17.8%
tx 4785
 
12.3%
wa 4579
 
11.8%
fl 1851
 
4.8%
co 1673
 
4.3%
mi 1384
 
3.6%
ny 1370
 
3.5%
mo 1284
 
3.3%
nh 1264
 
3.3%
tn 1212
 
3.1%
Other values (25) 12480
32.2%

Most occurring characters

ValueCountFrequency (%)
A 14147
18.2%
C 10462
13.5%
T 7863
10.1%
N 7241
9.3%
W 5336
 
6.9%
O 4904
 
6.3%
X 4785
 
6.2%
M 4598
 
5.9%
I 3319
 
4.3%
L 2362
 
3.0%
Other values (12) 12587
16.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77604
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14147
18.2%
C 10462
13.5%
T 7863
10.1%
N 7241
9.3%
W 5336
 
6.9%
O 4904
 
6.3%
X 4785
 
6.2%
M 4598
 
5.9%
I 3319
 
4.3%
L 2362
 
3.0%
Other values (12) 12587
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 77604
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14147
18.2%
C 10462
13.5%
T 7863
10.1%
N 7241
9.3%
W 5336
 
6.9%
O 4904
 
6.3%
X 4785
 
6.2%
M 4598
 
5.9%
I 3319
 
4.3%
L 2362
 
3.0%
Other values (12) 12587
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14147
18.2%
C 10462
13.5%
T 7863
10.1%
N 7241
9.3%
W 5336
 
6.9%
O 4904
 
6.3%
X 4785
 
6.2%
M 4598
 
5.9%
I 3319
 
4.3%
L 2362
 
3.0%
Other values (12) 12587
16.2%

City_y
Text

Distinct189
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
2024-01-09T19:22:00.750256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length32
Median length19
Mean length7.5818772
Min length3

Characters and Unicode

Total characters294192
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowNatrona
2nd rowKing
3rd rowPierce
4th rowNatrona
5th rowKing
ValueCountFrequency (%)
los 2441
 
5.3%
angeles 2441
 
5.3%
king 2212
 
4.8%
dallas 2081
 
4.5%
san 1334
 
2.9%
maricopa 1048
 
2.3%
harris 938
 
2.0%
orange 888
 
1.9%
st 845
 
1.8%
alameda 765
 
1.6%
Other values (195) 31373
67.7%
2024-01-09T19:22:00.997676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 35731
 
12.1%
e 24277
 
8.3%
n 20583
 
7.0%
o 19757
 
6.7%
i 18892
 
6.4%
s 17265
 
5.9%
l 17008
 
5.8%
r 16918
 
5.8%
t 9878
 
3.4%
g 9283
 
3.2%
Other values (44) 104600
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 237133
80.6%
Uppercase Letter 46491
 
15.8%
Space Separator 7564
 
2.6%
Other Punctuation 845
 
0.3%
Open Punctuation 728
 
0.2%
Close Punctuation 728
 
0.2%
Dash Punctuation 703
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 35731
15.1%
e 24277
10.2%
n 20583
8.7%
o 19757
8.3%
i 18892
 
8.0%
s 17265
 
7.3%
l 17008
 
7.2%
r 16918
 
7.1%
t 9878
 
4.2%
g 9283
 
3.9%
Other values (16) 47541
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 5632
12.1%
L 5130
11.0%
D 4953
10.7%
A 4029
8.7%
M 3227
 
6.9%
H 3109
 
6.7%
C 2949
 
6.3%
K 2713
 
5.8%
B 2671
 
5.7%
W 2329
 
5.0%
Other values (13) 9749
21.0%
Space Separator
ValueCountFrequency (%)
7564
100.0%
Other Punctuation
ValueCountFrequency (%)
. 845
100.0%
Open Punctuation
ValueCountFrequency (%)
( 728
100.0%
Close Punctuation
ValueCountFrequency (%)
) 728
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 703
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 283624
96.4%
Common 10568
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 35731
 
12.6%
e 24277
 
8.6%
n 20583
 
7.3%
o 19757
 
7.0%
i 18892
 
6.7%
s 17265
 
6.1%
l 17008
 
6.0%
r 16918
 
6.0%
t 9878
 
3.5%
g 9283
 
3.3%
Other values (39) 94032
33.2%
Common
ValueCountFrequency (%)
7564
71.6%
. 845
 
8.0%
( 728
 
6.9%
) 728
 
6.9%
- 703
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 293760
99.9%
None 432
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 35731
 
12.2%
e 24277
 
8.3%
n 20583
 
7.0%
o 19757
 
6.7%
i 18892
 
6.4%
s 17265
 
5.9%
l 17008
 
5.8%
r 16918
 
5.8%
t 9878
 
3.4%
g 9283
 
3.2%
Other values (43) 104168
35.5%
None
ValueCountFrequency (%)
ñ 432
100.0%

Interactions

2024-01-09T19:21:56.640065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2024-01-09T19:21:55.453070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:56.057449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:56.529942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:57.046175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:53.423916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:53.960472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:54.500633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:54.991803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:55.513547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:56.119127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-01-09T19:21:56.585190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2024-01-09T19:22:01.069215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
EmployeeListPriceNetSalesOrderQuantitySalesSaleswithStandardStandardCostStateStateCDState_duplicated_0UnitPriceproductcategoryproductsubcategorysaleterritoryzip
Employee1.0000.025-0.029-0.0580.0010.0030.0240.6360.6360.6360.0250.0310.0870.698-0.068
ListPrice0.0251.000-0.306-0.1730.9110.9160.9900.0950.0950.0950.9980.5320.4650.0490.020
NetSales-0.029-0.3061.0000.076-0.284-0.357-0.3770.0250.0250.025-0.2980.1480.1060.0140.014
OrderQuantity-0.058-0.1730.0761.0000.2020.183-0.1760.0490.0490.049-0.1760.1750.1140.0220.045
Sales0.0010.911-0.2840.2021.0000.9920.9040.0390.0390.0390.9120.2530.1590.0290.032
SaleswithStandard0.0030.916-0.3570.1830.9921.0000.9200.0290.0290.0290.9130.2110.1280.0210.031
StandardCost0.0240.990-0.377-0.1760.9040.9201.0000.1020.1020.1020.9890.5390.4720.0630.016
State0.6360.0950.0250.0490.0390.0290.1021.0001.0001.0000.0170.1100.0921.0000.066
StateCD0.6360.0950.0250.0490.0390.0290.1021.0001.0001.0000.0160.1100.0920.9990.073
State_duplicated_00.6360.0950.0250.0490.0390.0290.1021.0001.0001.0000.0170.1100.0920.9990.066
UnitPrice0.0250.998-0.298-0.1760.9120.9130.9890.0170.0160.0171.0000.5260.4530.0460.018
productcategory0.0310.5320.1480.1750.2530.2110.5390.1100.1100.1100.5261.0001.0000.0260.006
productsubcategory0.0870.4650.1060.1140.1590.1280.4720.0920.0920.0920.4531.0001.0000.1100.011
saleterritory0.6980.0490.0140.0220.0290.0210.0631.0000.9990.9990.0460.0260.1101.0000.330
zip-0.0680.0200.0140.0450.0320.0310.0160.0660.0730.0660.0180.0060.0110.3301.000

Missing values

2024-01-09T19:21:57.150042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-09T19:21:57.305590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

productcategoryproductsubcategorysaleterritoryzipStateCustomerEmployeeOrderCountOrderDateStandardCostUnitPriceListPriceSaleswithStandardNetSalesOrderQuantitySalesCountry_yTerritoryState_duplicated_0StateCDCity_y
0ClothingCapsNorthwest82601WyomingFrançois FerrierDavid Campbell15/31/2011 0:006.92235.18658.644220.7669-5.2074315.5595USCasperWyomingWYNatrona
1ClothingCapsNorthwest98104WashingtonRichard BreadyDavid Campbell15/31/2011 0:006.92235.18658.644213.8446-3.4716210.3730USSeattleWashingtonWAKing
2ComponentsMountain FramesNorthwest98371WashingtonCarolyn FarinoDavid Campbell15/31/2011 0:00739.0410714.70431191.1739739.0410-24.33671714.7043USPuyallupWashingtonWAPierce
3ClothingJerseysNorthwest82601WyomingFrançois FerrierDavid Campbell15/31/2011 0:0038.492328.840448.067376.9846-19.3038257.6808USCasperWyomingWYNatrona
4ClothingJerseysNorthwest98104WashingtonRichard BreadyDavid Campbell15/31/2011 0:0038.492328.840448.067376.9846-19.3038257.6808USSeattleWashingtonWAKing
5ClothingJerseysNorthwest98366WashingtonPeggy JusticeDavid Campbell15/31/2011 0:0038.492328.840448.067376.9846-19.3038257.6808USPort OrchardWashingtonWAKitsap
6ClothingJerseysNorthwest98366WashingtonPeggy JusticeDavid Campbell15/31/2011 0:0038.492328.840448.067338.4923-9.6519128.8404USPort OrchardWashingtonWAKitsap
7ComponentsRoad FramesNorthwest98366WashingtonPeggy JusticeDavid Campbell15/31/2011 0:00352.1394356.8980594.8300704.27889.51722713.7960USPort OrchardWashingtonWAKitsap
8ClothingSocksNorthwest82601WyomingFrançois FerrierDavid Campbell15/31/2011 0:003.39635.70009.500020.377813.8222634.2000USCasperWyomingWYNatrona
9ClothingSocksNorthwest98104WashingtonRichard BreadyDavid Campbell15/31/2011 0:003.39635.70009.500020.377813.8222634.2000USSeattleWashingtonWAKing
productcategoryproductsubcategorysaleterritoryzipStateCustomerEmployeeOrderCountOrderDateStandardCostUnitPriceListPriceSaleswithStandardNetSalesOrderQuantitySalesCountry_yTerritoryState_duplicated_0StateCDCity_y
38792BikesTouring BikesSoutheast34205FloridaChris BidelmanTsvi Reiter15/1/2014 0:00461.4448445.4100742.35922.8896-32.0696002890.820000USBradentonFloridaFLManatee
38793BikesTouring BikesSoutheast33602FloridaLiam FriedlandTsvi Reiter15/1/2014 0:00461.4448445.4100742.35461.4448-16.0348001445.410000USTampaFloridaFLHillsborough
38794BikesTouring BikesSoutheast27603North CarolinaReinout HillmannTsvi Reiter15/1/2014 0:00461.4448445.4100742.35461.4448-16.0348001445.410000USRaleighNorth CarolinaNCWake
38795AccessoriesBottles and CagesSoutheast34205FloridaChris BidelmanTsvi Reiter15/1/2014 0:001.86632.89424.9926.128213.5802241439.708424USBradentonFloridaFLManatee
38796ClothingShortsSoutheast37801TennesseeElizabeth CatalanoTsvi Reiter15/1/2014 0:0026.176341.994069.9978.528947.4531003125.982000USMaryvilleTennesseeTNBlount
38797ClothingShortsSoutheast30030GeorgiaPamela CoxTsvi Reiter15/1/2014 0:0026.176341.994069.99209.4104126.5416008335.952000USDecaturGeorgiaGADeKalb
38798ClothingShortsSoutheast33127FloridaStephanie ConroyTsvi Reiter15/1/2014 0:0026.176341.994069.99157.057894.9062006251.964000USMiamiFloridaFLMiami-Dade
38799ClothingShortsSoutheast30030GeorgiaPamela CoxTsvi Reiter15/1/2014 0:0026.176341.994069.9978.528947.4531003125.982000USDecaturGeorgiaGADeKalb
38800ClothingShortsSoutheast30030GeorgiaPamela CoxTsvi Reiter15/1/2014 0:0026.176341.994069.99130.881579.0885005209.970000USDecaturGeorgiaGADeKalb
38801ClothingShortsSoutheast33127FloridaStephanie ConroyTsvi Reiter15/1/2014 0:0026.176341.994069.9978.528947.4531003125.982000USMiamiFloridaFLMiami-Dade

Duplicate rows

Most frequently occurring

productcategoryproductsubcategorysaleterritoryzipStateCustomerEmployeeOrderCountOrderDateStandardCostUnitPriceListPriceSaleswithStandardNetSalesOrderQuantitySalesCountry_yTerritoryState_duplicated_0StateCDCity_y# duplicates
1175BikesRoad BikesCentral55056MinnesotaKim AbercrombieJillian Carson110/31/2011 0:00486.7066419.4589699.09821460.1198-201.743131258.3767USNorth BranchMinnesotaMNChisago8
1755BikesRoad BikesNorthwest97005OregonJoy KoskiPamela Ansman-Wolfe110/31/2011 0:00486.7066419.4589699.09821460.1198-201.743131258.3767USBeavertonOregonORWashington8
1760BikesRoad BikesNorthwest97005OregonJoy KoskiPamela Ansman-Wolfe14/30/2012 0:00486.7066419.4589699.0982973.4132-134.49542838.9178USBeavertonOregonORWashington8
2676BikesRoad BikesSouthwest91016CaliforniaJean JordanShu Ito18/31/2011 0:00486.7066419.4589699.0982486.7066-67.24771419.4589USMonroviaCaliforniaCALos Angeles8
1575BikesRoad BikesNortheast6512ConnecticutSara BreerMichael Blythe18/1/2011 0:00486.7066419.4589699.0982486.7066-67.24771419.4589USNew HavenConnecticutCTNew Haven7
1805BikesRoad BikesNorthwest98104WashingtonOrlando GeePamela Ansman-Wolfe13/30/2012 0:00486.7066419.4589699.0982486.7066-67.24771419.4589USSeattleWashingtonWAKing7
1993BikesRoad BikesSoutheast27104North CarolinaBrannon JonesTsvi Reiter13/30/2012 0:00486.7066419.4589699.0982486.7066-67.24771419.4589USWinston-SalemNorth CarolinaNCForsyth7
2801BikesRoad BikesSouthwest92867CaliforniaJessie ValerioLinda Mitchell13/30/2012 0:00486.7066419.4589699.0982486.7066-67.24771419.4589USOrangeCaliforniaCAOrange7
2932BikesRoad BikesSouthwest95354CaliforniaFrances AdamsShu Ito18/31/2011 0:00486.7066419.4589699.0982486.7066-67.24771419.4589USModestoCaliforniaCAStanislaus7
1116BikesRoad BikesCentral53182WisconsinLee ChaplaJillian Carson110/30/2012 0:00486.7066469.7940782.9900973.4132-33.82522939.5880USUnion GroveWisconsinWIRacine6